System of Systems Interoperability Machine Learning Model

Document identifier: oai:DiVA.org:ltu-76229
Keyword: Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Other Electrical Engineering, Electronic Engineering, Information Engineering, Teknik och teknologier, Elektroteknik och elektronik, Annan elektroteknik och elektronik, System of systems interoperability, Machine learning, Message translation, Information interoperability, Autoencoder, Cyber-physical systems, Industrial Electronics, Industriell elektronik
Publication year: 2019
Abstract:

Increasingly flexible and efficient industrial processes and automation systems are developed by integrating computational systems and physical processes, thereby forming large heterogeneous systems of cyber-physical systems. Such systems depend on particular data models and payload formats for communication, and making different entities interoperable is a challenging problem that drives the engineering costs and time to deployment. Interoperability is typically established and maintained manually using domain knowledge and tools for processing and visualization of symbolic metadata, which limits the scalability of the present approach. The vision of next generation automation frameworks, like the Arrowhead Framework, is to provide autonomous interoperability solutions. In this thesis the problem to automatically establish interoperability between cyber-physical systems is reviewed and formulated as a mathematical optimisation problem, where symbolic metadata and message payloads are combined with machine learning methods to enable message translation and improve system of systems utility. An autoencoder based implementation of the model is investigated and simulation results for a heating and ventilation system are presented, where messages are partially translated correctly by semantic interpolation and generalisation of the latent representations. A maximum translation accuracy of 49% is obtained using this unsupervised learning approach. Further work is required to improve the translation accuracy, in particular by further exploiting metadata in the model architecture and autoencoder training protocol, and by considering more advanced regularization methods and utility optimization.

Authors

Jacob Nilsson

Luleå tekniska universitet; EISLAB
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Fredrik Sandin

Luleå tekniska universitet; EISLAB
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Sheraz Ahmed

German Research Center for Artificial Intelligence
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